Who Form 8854 Attributable 2023-2024

Hi, I'm Mike Marin. In this video, we'll discuss a package for calculating relative risk, odds ratios, and risk differences using the relative risk odds ratio and risk difference. These measures are used to determine the direction and strength of the association between two categorical variables. We will be working with the lung capacity data that was introduced earlier in this series of videos. I've already imported the data into R and attached it. We will explore the relationship between gender and smoking. To start, we will produce a two-way table and save it in the object "keyAB". We can use the "table" command and specify the variables "gender" and "smoke" to create the table. To visually examine the relationship between these two variables, we can produce a bar plot of the table. By setting the "beside" argument to true, the bar plots will be placed side-by-side. We can also set the "legend" argument to true to display a default legend. From the bar plot, it appears that the non-smoking group has more males than females, while the smoking group has more females than males. This suggests that there may be a relationship between these two variables. In the previous video, we learned how to conduct the chi-square test of Independence. However, this test does not provide information about the strength or direction of the association. To calculate measures of association, such as relative risk, odds ratios, and attributable risk, we will use the "epiR" package. Note that there are other packages available for these calculations as well. After installing the "epiR" package, we can load the library for it. For instructions on installing packages, refer to my earlier video in series 1 on installing packages in R. To access the help menu for the "epiR" package, you can type "help(package = 'epiR')". We will use the "table"...